def get_sentiment(self, cumulative=False):
'''
Calculates the sentiment of the cluster. It returns
a list of tuples (date, value) where date value is the accumulated sentiment
of that date.
'''
emotional_rollercoaster = []
tsa = TwitterSemanticAnalyser()
for document in self.document_dict.values():
sentiment = tsa.extract_sentiment(' '.join(token for token in document.tokens))
emotional_rollercoaster.append( (document.date, sentiment) )
#It's important to sort this list otherwise itertools will npt work.
#The we group emotion scores by date. t[1][1] is the score of a document at time d.
x = sorted(emotional_rollercoaster)
grouped_emotions = [(d, sum([float(t[1][1]) for t in g])) for d, g in itertools.groupby(x, lambda x: x[0])]
return grouped_emotions
def get_sentiment(self, cumulative=False):
'''
Calculates the sentiment of the cluster. It returns
a list of tuples (date, value) where date value is the accumulated sentiment
of that date.
'''
emotional_rollercoaster = []
tsa = TwitterSemanticAnalyser()
for document in self.document_dict.values():
sentiment = tsa.extract_sentiment(' '.join(token for token in document.tokens))
emotional_rollercoaster.append( (document.date, sentiment) )
#It's important to sort this list otherwise itertools will npt work.
#The we group emotion scores by date. t[1][1] is the score of a document at time d.
x = sorted(emotional_rollercoaster)
grouped_emotions = [(d, sum([float(t[1][1]) for t in g])) for d, g in itertools.groupby(x, lambda x: x[0])]
return grouped_emotions
def test_sentiment_extraction(self):
tsa = TwitterSemanticAnalyser()
calculated_sad = tsa.extract_sentiment(tweet6)
calculated_happy = tsa.extract_sentiment(tweet7)
self.assertEqual(('negative', '-0.200008'), calculated_sad)
self.assertEqual(('positive', '0.61373'), calculated_happy)
def test_sentiment_extraction(self):
tsa = TwitterSemanticAnalyser()
calculated_sad = tsa.extract_sentiment(tweet6)
calculated_happy = tsa.extract_sentiment(tweet7)
self.assertEqual(('negative', '-0.200008'), calculated_sad)
self.assertEqual(('positive', '0.61373'), calculated_happy)